Pet food recommendation devices and methods

ABSTRACT

A system or method is provided that includes receiving a pet image. The pet image may depict a pet and may be received from a user device. The system or method may further analyze the pet image with a pet image recognition model to determine one or more pet characteristics of the pet. In certain embodiments, analyzing the pet image may further include identifying one or more image characteristics of the pet image. The system or method may further analyze the pet characteristics with a pet food recommendation model to generate a pet food recommendation for the pet.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 62/800,199 filed Feb. 1, 2019, the disclosure of which isincorporated in its entirety herein by this reference.

FIELD

The present disclosure relates generally to methods and devices for petfoods. More specifically, the present disclosure relates to methods anddevices for providing a pet food recommendation and/or a pet feedingrecommendation.

BACKGROUND

Many pet owners care deeply for their pets and understand that decisionsrelated to what and how much food to feed their pets can contribute tolonger, happier, and healthier lives together. However, thecharacteristics of each pet and the circumstances and preferences of thepet and owner are unique. Moreover, those characteristics,circumstances, and preferences change over time. For this reason,consumers seek choices in what to feed their pets and information tohelp guide their decisions. However, the plethora of products andinformation sources can complicate this process and leave consumers withunresolved emotions and questions. Lack of information or a change inconditions can also mean that a feeding decision optimal at one point ina pet's life may be less than optimal at another.

Existing solutions are primarily provided by retailers or manufacturers.Retailer solutions rely on user defined inputs and are largely filterbased, static, reactive, and impersonal. Filters may narrow a selectionbased on characteristics of the products available, but they do notcapture information sufficient to provide the optimal solution for theindividual pet. Moreover, existing solutions do not provide proactiverecommendations that account for changes in the pet over time.Manufacturer product selectors feature many of the same characteristicsand drawbacks. Even in instances where a manufacturer may capture moreinformation about the individual pet, recommendations may still onlyinclude product recommendations and may lack actionable recommendationsfor owners. Further, these recommendations may only be suitable for agiven point in time (i.e., the recommendations are not proactive and/ordo not change over time). Also, the recommendations are generallylimited by the breadth of the portfolio of a single pet foodmanufacturer.

SUMMARY

The present disclosure provides advantages and solutions to problems inexisting technologies for making pet food recommendations. In thisregard, a pet food recommendation system may capture the most relevantinformation about a pet owner and a pet to streamline the decisionprocess and provide personalized pet food recommendations and/or petfeeding recommendations. Personalized pet food recommendations and/orpet feeding recommendations may be provided initially and/or proactivelythroughout the pet's life. For example, a pet food and/or pet feedingrecommendation can help a pet owner transition from a current diet andfeeding recommendation to one that may help to maintain or change bodycondition, with the ultimate goal of achieving ideal body condition forthat individual pet.

In an embodiment, personalized pet food recommendations and/or petfeeding recommendations may consider a body condition of the pet, avariance to an expected weight of the pet, an activity level of the pet,a morphology of the pet, a breed, and/or a breed size of the pet. Forexample, a pet's breed can provide information about certaincharacteristics and general parameters one can expect to see withinthose characteristics, such as weight range and activity level, but eachindividual pet will have a unique weight and/or activity. Thepersonalized pet food recommendations and/or pet feeding recommendationsmay further consider pet owner preferences, pet needs and/or petpreferences (e.g., the presence and/or absence of certain ingredients,for example grains or protein type, in the pet food, the degree to whichthe product is made from natural ingredients, the incompatibility of afood with a pet due to a potential for allergic response or sensitivity,and the retail channel of the pet food product) to personalize therecommendation for the pet and the pet owner. Additionally, the pet foodrecommendations and/or pet feeding recommendations may further considerbody conformation characteristics and/or morphometric measurements asdescribed in U.S. Pat. No. 8,091,509 and U.S. Patent ApplicationPublication No. 2017/0042194, each of which is incorporated herein byreference.

In an embodiment, the pet may be a dog, a cat, a bird, a rodent, ahorse, a pig, a fish, a reptile and/or any other household pet and/ordomesticated animal.

Accordingly, one aspect of the disclosure is directed to a methodcomprising: receiving a pet image depicting a pet; analyzing, with a petimage recognition model, the pet image to determine one or more petcharacteristics of the pet; receiving one or more pet-related datainputs; analyzing, with a pet food recommendation model, the petcharacteristics and pet-related data inputs to generate a pet foodrecommendation and/or pet feeding recommendation for the pet. In someembodiments, the pet characteristics comprise one or more of a breed, abreed size, a pet size, a body condition, a life stage, an activitylevel, a pet gender, a pet gender status, and a weight of the pet. Insome embodiments, the pet-related data inputs may comprise one or moreof a pet preference, a user preference, and/or pet characteristics. Inone embodiment, the pet and/or user preference comprises one or more ofa grain preference, a protein preference, a food texture, a naturalingredient preference, and a shopping preference. In another embodiment,the pet and/or user preference is selected from the group consisting ofa grain preference, a protein preference, a food texture, a naturalingredient preference, and a shopping preference. In some embodiments,the pet-related data inputs comprise a pet characteristic. In oneembodiment, one or more pet characteristics are determined by the petimage recognition model and one or more additional pet characteristicsare input by the user.

In some embodiments, the pet image is received from a user device. Inone embodiment, the user device is a mobile device, for example, amobile phone. In another embodiment, the user device is a laptop or adesktop computer.

In some embodiments, the analyzing the pet image further comprises:analyzing the pet image to identify one or more image characteristics ofthe pet image; and determining the one or more pet characteristics basedon the image characteristics. In some embodiments the imagecharacteristics are selected from the group consisting of an outline ofthe pet, an area enclosed by the outline of the pet, the percentage ofpixels of the pet image occupied by the pet, one or more geometricdimensions of the pet within the pet image. In some embodiments, thegeometric dimensions are selected from length, height, distance fromfoot to shoulder, distance from back to stomach, chest breadth, chestdepth.

In some embodiments, the method further comprises: filtering a list ofpet food products based on at least one user preference to create afiltered list of pet food product. In some embodiments the at least oneuser preference comprises at least one preference selected from thegroup consisting of a grain preference, a protein preference, a foodtexture preference, a natural ingredient preference, and a shoppingpreference.

In some embodiments, the pet characteristics comprises one or morecharacteristics selected from the group consisting of a breed, a breedsize, a pet size, a body condition, a life stage, an activity level, apet gender, a pet gender status, and a weight of the pet.

In some embodiments, the method further comprises calculating one ormore variances selected from the group consisting of a breed sizevariance, a pet size variance, a body condition variance, a life stagevariance, an activity level variance, and a pet weight variance.

In some embodiments, the method further comprises calculating aplurality of variances to generate a plurality of calculated variances;and scoring the list of food products based on a sum of the calculatedvariances to identify one or more recommended pet food products.

In some embodiments, the method further comprises presenting the petfood recommendation to a user, wherein the pet food recommendationcontains a plurality of pet food products; receiving a selection fromthe user of a selected pet food product from the plurality of pet foodproducts; and generating a pet feeding recommendation based on theselected pet food product. In one embodiment, the pet feedingrecommendation is generated based on a caloric density of the selectedpet food product.

In some embodiments, the method further comprises storing behavioraldata of the pet and/or a user in a historical database; and training oneor both of the pet image recognition model and the pet foodrecommendation model based on the behavioral data stored in thehistorical database.

Another aspect of the disclosure is directed to a system comprising: aprocessor; and a memory storing instructions which, when executed by theprocessor, cause the processor to receive a pet image depicting a pet;analyze, with a pet image recognition model, the pet image to determineone or more pet characteristics of the pet; receive one or morepet-related data inputs; analyze with a pet food recommendation model,the pet characteristics and pet-related data inputs to generate a petfood recommendation for the pet and/or pet feeding recommendation forthe pet.

In some embodiments of the system, the memory stores furtherinstructions which, when executed by the processor, cause the processorto: analyze the pet image to identify one or more image characteristicsof the pet image; and determine the one or more pet characteristicsbased on the image characteristics.

In some embodiments of the system, the memory stores furtherinstructions which, when executed by the processor, cause the processorto: filter a list of pet food products based on at least one userpreference to create a filtered list of pet food products.

In some embodiments of the system, the pet characteristics comprise oneor more characteristics selected from the group consisting of a breed, abreed size, a body condition, a life stage, an activity level, a petsize, a pet gender, a pet gender status, and a weight of the pet.

In some embodiments of the system, the memory stores furtherinstructions which, when executed by the processor, cause the processorto: calculate one or more variances selected from the group consistingof a breed size variance, a pet size variance, a body conditionvariance, a life stage variance, an activity level variance, and a petweight variance.

In some embodiments of the system, the memory stores furtherinstructions which, when executed by the processor, cause the processorto: calculate a plurality of variances to generate a plurality ofcalculated variances; and score a list of pet food products based on thesum of the calculated variances to identify one or more recommended petfood products.

In some embodiments of the system, the memory stores furtherinstructions which, when executed by the processor, cause the processorto: present the pet food recommendation to a user, wherein the pet foodrecommendation contains a plurality of pet food products; receive aselection from the user of a selected pet food product from theplurality of pet food products; and generate a pet feedingrecommendation based on the selected pet food product.

In some embodiments of the system, the memory stores furtherinstructions which, when executed by the processor, cause the processorto: store behavioral data of the pet and/or a user in a historicaldatabase; and train one or both of the image recognition model and thepet food recommendation model based on the behavioral data store in thehistorical database.

Another aspect of the invention is directed to a non-transitory,computer-readable medium storing instructions which, when executed by aprocessor, cause the processor to: receive a pet image depicting a pet;analyze, with an image recognition model, the pet image to determine onor more pet characteristics of the pet; receive one or more pet-relateddata inputs; analyze with a pet food recommendation model, the petcharacteristics and data inputs to generate a pet food recommendationfor the pet.

In an embodiment, the pet food recommendation comprises one or more petfood products. In an embodiment, the pet food recommendation comprises acomposition intended for consumption by a pet. In some embodiments, thepet food recommendation comprises one or more pet food products that apet consumes as a main meal, including but not limited to dry, wet,semi-moist, moist, and liquid food compositions. In another embodiment,the pet food recommendation comprises a pet treat. In one embodiment, atreat is a food for consumption by a pet that is intended as anoccasional reward or indulgence and not as the sole source of a pet'snutrition. In another embodiment, the pet food recommendation comprisesa pet supplement. In one embodiment, a pet supplement is a compositionfor oral consumption offered separately from formulated food or treats,intended for an effect on the pet beyond normal nutritional needs. Forexample, supplementary products for pets may include those that conferbenefits for the joints, skin, hair coat, and/or digestive system.

In an embodiment, personalized pet food recommendations and/or petfeeding recommendations may apply knowledge of food characteristics andattributes of pets and/or pet breeds to determine the best fit productand feeding recommendations.

In an embodiment, the personalized pet food recommendation and/or thepet feeding recommendation may utilize image recognition technology todetermine multiple pet factors (i.e., pet breed, pet size, pet bodycondition) in conjunction with machine learning models that areconfigured to generate food recommendations based upon data obtainedfrom an end user, data from the image recognition tools, and/or otherdata from an application of algorithms to account for an activity leveland a life stage of a pet. In an embodiment, the image utilized by theimage recognition tools is a static or still image (e.g. digital photo).In another embodiment, the image recognition technology utilizes aplurality of images or a dynamic image (e.g. multiple individual photosor a video).

In an embodiment, personalized pet food recommendations and/or petfeeding recommendations may be personalized and customized,incorporating a choice of pet food product as well as any previouslyentered pet variables (i.e., pet breed, pet size, pet body condition) togenerate a recommendation for a daily pet food intake. In an embodiment,the daily pet food intake may be adjusted (e.g., to achieve a goal ofpet weight gain, pet weight loss, or pet weight maintenance).

In an embodiment, personalized pet food recommendations and/or petfeeding recommendations may utilize artificial intelligence (e.g., imagerecognition) to determine a pet species and/or a pet breed toincorporate that information into the pet food recommendation and/or thepet feeding recommendation.

In an embodiment, personalized pet food recommendations and/or petfeeding recommendations may utilize machine learning to optimize the petfood recommendation and pet feeding recommendation over time. Forexample, machine learning models may be configured to generate one orboth of the pet food recommendation and the pet feeding recommendationand may incorporate feedback on the quality of recommendations andactual performance information provided by a pet owner and/or other user(e.g., a veterinarian).

In an embodiment, a device may be configured to provide personalized petfood recommendations and/or pet feeding recommendations. In such anembodiment, the device may be further configured to track theachievement of goals related to pet body condition over time andinfluence the pet food product selection and pet food feeding regimenbased on the goals.

In an embodiment, personalized pet food recommendations and/or petfeeding recommendations may utilize connected pet devices (e.g.,collars, bowls, scoops, litter boxes, water dispensers, cameras, fences,scales, bins, mats, beds) to automatically collect and transmit dataused to provide the personalized pet food recommendations and/or petfeeding recommendations. The data used to provide the personalized petfood recommendations and/or pet feeding recommendations may be relevantto identifying changes in pet food product or pet food feeding choicesover time. Such a configuration may eliminate sources of error or lapsesin time inherent with manual data input (e.g., data input by a userand/or pet owner), which may enable more proactive personalizing of petfood recommendations and pet feeding recommendations.

In an embodiment, personalized pet food product recommendations and/orpet feeding recommendations may utilize artificial intelligence andmachine learning to assess pet waste characteristics and translate thepet waste characteristics into the personalized pet food recommendationsand/or pet feeding recommendations.

The features and advantages described herein are not all-inclusive and,in particular, many additional features and advantages will be apparentto one of ordinary skill in the art in view of the figures anddescription. Moreover, it should be noted that the language used in thespecification has been principally selected for readability andinstructional purposes, and not to limit the scope of the inventivesubject matter.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a flowchart of an embodiment of a system and methodfor providing personalized pet food recommendations and/or pet feedingrecommendations according to the present disclosure.

FIG. 2 illustrates an embodiment of an application diagram according tothe present disclosure.

FIG. 3 illustrates an embodiment of a summary communication forproviding personalized pet food recommendations and/or pet feedingrecommendations according to the present disclosure.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Detailed embodiments of devices and methods are disclosed herein.However, it is to be understood that the disclosed embodiments aremerely exemplary of the devices and methods, which may be embodied invarious forms. Therefore, specific functional details disclosed hereinare not to be interpreted as limiting, but merely as a basis for theclaims as a representative example for teaching one skilled in the artto variously employ the present disclosure.

As used herein, “about,” “approximately,” and “substantially” areunderstood to refer to numbers in a range of numerals, for example therange of −10% to +10% of the referenced number, preferably −5% to +5% ofthe referenced number, more preferably −1% to +1% of the referencednumber, most preferably −0.1% to +0.1% of the referenced number. Allnumerical ranges herein should be understood to include all integers,whole or fractions, within the range. Moreover, these numerical rangesshould be construed as providing support for a claim directed to anynumber or subset of numbers in that range. For example, a disclosure offrom 1 to 10 should be construed as supporting a range of from 1 to 8,from 3 to 7, from 1 to 9, from 3.6 to 4.6, from 3.5 to 9.9, and soforth.

As used herein, the singular forms “a,” “an” and “the” include pluralreferents unless the context clearly dictates otherwise. For example,reference to “an ingredient” or “a method” includes a plurality of such“ingredients” or “methods.” The term “and/or” used in the context of “Xand/or Y” should be interpreted as “X,” or “Y,” or “X and Y.” Similarly,“at least one of X or Y” should be interpreted as “X,” or “Y,” or “bothX and Y.”

The preferred embodiments relate to devices and methods that providepersonalized pet food recommendations and/or pet feeding recommendationsto a user and/or pet owner.

As described herein, robust data at the individual pet and person level,along with artificial intelligence and machine learning, may assist thedevices and methods disclosed herein. In broad, basic terms, “artificialintelligence” refers to techniques that enable computers to mimic thehuman capabilities of sensing, problem solving, acting, adapting, andimproving over time with experience. Image recognition and naturallanguage processing may be considered two specific examples or types ofartificial intelligence that convert aspects of their environment intopieces of information that may be provided as inputs intocomputer-implemented algorithms. Machine learning techniques may helpadapt and improve an algorithm's performance of a task or an ability toachieve a desired outcome over time based on historical data.

As used herein, “species” refers to the commonly associated name of thespecies of the pet. For example, a Golden Retriever is of the species“dog,” a Maine Coon is of the species “cat,” and a Blue Tang is of thespecies “fish.”

As used herein, “life stage” refers to the relative stage of a pet inthe pet's lifecycle. For example, dogs may have life stages including“puppy,” “adult” and “senior.” Cats may have life stages including“kitten,” “adult,” and “senior.”

As used herein, “breed size” refers to the relative size of a given petbreed when compared to other pet breeds of the same species. Examplebreed sizes may include toy, small, medium, large, and giant. Forexample, a Chihuahua may be considered a toy breed, while a Great Danemay be considered a giant breed. In some instances, a pet may not beeasily categorized within a specific breed. In such instances, a pet maybe more generally categorized according to size (e.g. small, medium orlarge pet).

As used herein, “breed size variance” or “pet size variance” refers to ameasure of how closely a given pet food product is designed for aparticular breed size or pet size. Pets of a different breed size or petsize may have different needs. For example, dogs of a toy or small sizemay need a lower caloric intake, typically expressed as energy intakeover time, e.g. 150 kcal/day. Additionally, the caloric density of thefood which reflects the caloric content in a given volume, e.g. 150kcal/cup, influences the recommendation of how much of the food to feedin a given time to achieve the caloric intake. Comparatively, dogs of alarge or giant breed size may need food that helps account for certainhealth characteristics correlated with their size (e.g., foods withincluded supplementation to account for hip or digestive issues commonwith large or giant-sized dog breeds).

For example, the breed size variance (BSV) may be calculated for a petfood product relative to a given breed size according to the formula:

${BSV} = \frac{PF}{\left( {M + 1} \right)}$where PF is a prioritization factor, and where M is determined based onthe breed size for which the pet food product is intended relative tothe given breed size. For example, for a specified breed size, foodproducts targeting the specified breed size may be assigned an M valueof 0, food products targeting all breed sizes may be assigned in an Mvalue of 1, and food products targeting breed sizes neighboring thespecified breed size may receive an M value of 2. As another example, insuch an implementation, where the specified breed size is small, the petfood products targeted to the small breed size will receive an M valueof 0, pet food products targeting all breed sizes will receive an Mvalue of 1, and pet food products targeting toy and medium breed sizeswill receive an M value of 2 as shown in the table below:

Breed Size M Value Small 0 All Breeds 1 Small and Medium 2The prioritization factor may reflect a relative importance of the breedsize variance in subsequent calculations (e.g., when scoring pet foodproducts to generate a pet food recommendation). Pet size variance maybe similarly calculated.

In certain implementations, where breed size variance or pet sizevariance is highly important to subsequent calculations, theprioritization factor may be set at 10 (out of a maximum of 10). In suchan implementation, and continuing the preceding example, pet foodproducts targeting the small breed size will have a breed size varianceof BSV =10/(0+1)=10, pet food products targeting all breed sizes willhave a breed size variance of BSV=10/(1+1)=5, and pet food productstargeting toy and medium breed sizes will have a breed size variance ofBSV=10/(2+1)=3.33. Pet food products targeting further than neighboringbreed sizes (i.e., large and giant breed sizes for small breedcalculations) may be ignored in the breed size variance calculation.

As used herein, “body condition variance” refers to a measure of howclosely a given pet food product is designed for pets of a given bodycondition. For example, pets that are overweight may require a lowercaloric intake than pets at an ideal body weight, so pet food productsdesigned for overweight pets may have lower calorie density. With allother conditions being equal, a lower caloric intake and/or caloricdensity may help the pet lose weight to restore the pet to an ideal bodyweight. Similarly, pets that are underweight may require a highercaloric intake than pets that are at an ideal body weight. Accordingly,pet food products designed for underweight pets may have a highercalorie density to help underweight pets gain weight and return to anideal body weight. Therefore, the body condition variance of a given petfood product may be calculated on the caloric density of the pet foodproduct.

For example, the body condition variance (BCV) may be calculated for agiven pet food product relative to the caloric density of the pet foodproduct according to the formula:

${BCV} = \frac{PF}{\left( \left| {{FAV} - M} \middle| {+ 1} \right. \right)}$where PF is a prioritization factor, FAV is a food assigned value and isdetermined based on the caloric density of the pet food product, and Mis selected based on a given body condition. For example, FAV may beassigned based on the caloric density (calories/cup) of the pet foodproduct according to the table below:

Calories/Cup 100 125 200 300 FAV 0 .25 1 2Similarly, the M value may be calculated based on a given body conditionaccording to the table below:

Body Condition M Value Overweight 0 Ideal 1 Underweight 2

The prioritization factor may reflect a relative importance of the bodycondition variance in subsequent calculations (e.g., when scoring petfood products to generate a pet food recommendation). In certainimplementations, where body condition variance is important tosubsequent calculations, but not as important as breed size variance,the prioritization factor may be set at 9 (out of a maximum of 10). Insuch implementations, for an overweight pet (i.e., M=0), the pet foodproduct with the highest caloric density would have a body conditionvariance of BCV=9/(|2−0|+1)=4.5 and the pet food product with the lowestcaloric density would have a body condition variance ofBCV=9/(|0-0|+1)=9.

As used herein, “life stage variance” refers to a comparison between thelife stage of the pet and the pet life stage for which a pet foodproduct is targeted. For example, younger pets may require food withhigher caloric density, as such pets are still growing and generallyvery active, whereas older pets may require food with a lower caloricdensity, as older pets may typically be less active. Additionally, olderpets may require food that accounts for health issues associated withtheir older age (e.g., joint issues, digestive issues, heart issues).The life stage variance may therefore be calculated relative to thepet's given life stage.

For example, the life stage variance (LSV) may be calculated accordingto the formula:

${LSV} = \frac{PF}{\left( {M + 1} \right)}$where PF is a prioritization factor, and where M is determined based onthe pet's life stage and the life stage for which the pet food productis intended. For example, similar to the breed size variance discussedabove, for a specified life stage, food products targeting the same lifestage may be assigned an M value of 0, food products targeting all lifestages may be assigned in M value of 1, and pet food products targetinglife stages the neighboring the specified life stage may receive fan Mvalue of 2. Pet food products intended for other life stages than thosediscussed here may be excluded from the calculation. As another example,in such an implementation, where the specified life stage is puppy orkitten, then pet food products intended for puppies or kittens areassigned an M value of 0, pet food products targeting all life stagesare assigned an M value of 1, and pet food products targeting the adultlife stage are assigned an M value of 2 as shown in the table below:

Life Stage M Value Puppy 0 All 1 Adult 2

Pet food products targeting the senior life stage may be excluded fromthe calculation and from potential pet food recommendations. Theprioritization factor may reflect a relative importance of the lifestage variance in subsequent calculations (e.g., when scoring pet foodproducts to generate a pet food recommendation). In certainimplementations, where life stage variance is relevant to subsequentcalculations, but less important than breed size variance and bodycondition variance, the prioritization factor may be set at 8 (out of amaximum of 10). In such implementations, and continuing the precedingexample, pet food products targeting puppies or kittens will have a lifestage variance of LSV=8/(0+1)=8, pet food products targeting all lifestages will have a life stage variance of LSV=8/(1+1)=4, and pet foodproducts targeting adult life stages will have a life stage variance ofLSV=8/(2+1)=2.67.

As used herein, “activity level variance” refers to a comparison betweenthe activity level of the pet and a pet food product's caloric content.Pets that are more active may generally require a higher caloric intaketo avoid losing too much weight and may therefore require pet foodproducts with higher calorie densities. Similarly, pets that are lessactive may generally require fewer calories to avoid gaining too muchweight and may therefore require pet food products with lower caloriedensities. The activity level variance for a pet food product maytherefore be calculated relative to a specified activity level.

For example, the activity level variance (ALV) may be calculated for agiven pet food product relative to an activity level according to theformula:

${ALV} = \frac{PF}{\left( \left| {{FAV} - M} \middle| {+ 1} \right. \right)}$where PF is a prioritization factor, FAV is a food assigned value and isdetermined based on the caloric density of the pet food product, and Mis selected based on a given activity level. For example, FAV may beassigned based on the caloric density (calories/cup) of the pet foodproduct according to the table provided above in connection with thebody condition variance. Similarly, the M value may be calculated basedon a given activity level according to the table below:

Active Level M Value Less Active 0 Semi Active 1 Active 2 Highly Active3The prioritization factor may reflect a relative importance of theactivity level variance in subsequent calculations (e.g., when scoringpet food products to generate a pet food recommendation). In certainimplementations, where activity level variance is less important thanbreed size variance, body condition variance, and life stage variance,the prioritization factor for activity level variance may be set at 5(out of a maximum of 10).

In such implementations, for a less active pet (i.e., M=0), the activitylevel variance for a pet food product with the highest caloric densitywould be ALV=5/(|3−0|+1)=1.25 and the activity level variance for a petfood product with the lowest caloric density would be ALV=5/(|0−0|+1)=5

As used herein, “pet weight variance” refers to a comparison between theactual weight of the pet and the expected weight of the pet for thebreed(s) of the pet and the caloric content of the pet food. Pets thatare overweight relative to their breeds may require pet food productswith lower caloric density than pets at an ideal weight for theirbreed(s). Similarly, pets that are underweight relative to theirbreed(s) may require foods with higher caloric density than pets at anideal weight. Accordingly, the pet weight variance may be calculatedrelative to a specified weight of a pet relative to its breed.

For example, the pet weight variance (PWV) may be calculated relative toa pet weight according to the formula:

${PWV} = \frac{PF}{\left( \left| {{FAV} - M} \middle| {+ 1} \right. \right)}$where PF is a prioritization factor, FAV is a food assigned value and isdetermined based on the caloric density of the pet food product, and Mis selected based on a given pet weight status. For example, FAV may beassigned based on the caloric density (calories/cup) of the pet foodproduct according to the table provided above in connection with thebody condition variance. Similarly, the M value may be calculated basedon a given activity level according to the table below:

Weight Condition M Value Overweight 0 Ideal 1 Underweight 2

In certain implementations, calculating the pet weight variance mayfurther include calculating the pet's weight condition. For a given petweight (PW), the weight condition (WC) may be calculated according tothe formula:

${WC} = \frac{PW}{ACW}$where ACW is the average calculated weight of the pet's breed. For petswith a single breed, the ACW may equal the average of the minimum andmaximum healthy weights for the breed. For pets with more than onebreed, the ACW may equal the average of each breed's minimum and maximumweights. If the WC is greater than 1, the pet may be consideredoverweight, if the WC is less than 1, the pet may be consideredunderweight, and if the WC is approximately 1 (e.g., 0.95-1.05), the petmay be considered at an ideal weight. The prioritization factor mayreflect a relative importance of the pet weight variance in subsequentcalculations (e.g., when scoring pet food products to generate a petfood recommendation). In certain implementations, where pet weightvariance is less important than breed size variance, body conditionvariance, life stage variance, and activity level variance, theprioritization factor for pet weight variance may be set at 1 (out of amaximum of 10).

In such implementations, for an overweight pet (i.e., M=0), the pet foodproduct with the highest caloric density would have a pet weightvariance of PWV=1/(|2−0|+1)=0.33 and the pet food product with thelowest caloric density would have a pet weight variance ofPWV=1/(|0−0|+1)=1.

As used herein, “grain preference” refers to a preference of the user orof the pet for the grain contents of a pet food product. In certainimplementations, the grain preference may include (i) that pet foodproducts containing grain are preferred, (ii) that pet food productscontaining grain are disfavored or that grain-free pet food products arepreferred, and (iii) no preference for the grain content of pet foodproducts.

As used herein, “protein preference” refers to a preference of the useror of the pet for protein contents of a pet food product. In certainimplementations, the protein preference may include (i) that pet foodproducts containing poultry (e.g. chicken, duck or turkey) arepreferred, (ii) that pet food products containing beef are preferred,(iii) that pet food products containing pork are preferred, (iv) thatpet food products containing fish are preferred, (v) that pet foodproducts containing plant-based proteins are preferred, and (vi) nopreference for the protein content of pet food products.

As used herein, “food texture preference” refers to a preference of theuser or of the pet for the texture of a pet food product. In certainimplementations, the food texture preference may include (i) that wetpet food products are preferred, (ii) that dry pet food product arepreferred, and (iii) no preference for the food texture of pet foodproducts.

As used herein, “natural ingredient preference” refers to a preferenceof the user or of the pet for the texture of a pet food product. Incertain implementations, the natural ingredient preference may include(i) a preference for pet food products that contain natural ingredients,(ii) a preference for pet food products without natural ingredients, and(iii) no preference for the natural ingredient contents of pet foodproducts.

As used herein, “shopping preference” refers to a preference of the userfor the shopping availability for a pet food product. In certainimplementations, the shopping preference may include (i) a preferencefor products that are available online (or both online and in stores),(ii) a preference for products that are available in stores (or both instores and online), and (iii) no preference for the shoppingavailability of pet food products.

The preferred embodiments relate to methods and devices for determininga pet food and/or a pet feeding schedule.

FIG. 1 generally illustrates an embodiment of a system 5 for determininga recommended pet food 21 for a pet 1. For example, a user 2, who mayown the pet 1, may desire to purchase food for the pet 1. However, theuser 2 may not know the exact type of food best suited for the needs ofthe pet 1 and/or or the recommended feeding schedule of the food for thepet 1. Accordingly, the user 2 may use the system 5 to determine the petfood recommendation and/or the pet feeding recommendation 21 for the pet1.

The system 5 may include an interface 22. In some embodiments, theinterface is a web interface. The web interface 22 may be implemented bya web interface server 23. For example, the web interface server 23 mayprovide the web interface 22 upon receiving a request from a user device11. The user device 11 may be implemented as, e.g., a cell phone, smartphone, smart watch, laptop, tablet, personal digital assistant (PDA),and/or desktop computer. The user device 11 may be capable of taking apet image 3 of the pet. In another embodiment, the user device 11 mayadditionally or alternatively store the pet image 3 of the pet 1. Theinterface 22 of the system 5 may be capable of receiving the pet image3. For example, the user 2 may be able to upload the pet image 3 to theinterface 22. The user device 11 may communicate with the interface 22and/or the interface server 23 via a network connection, such as anetwork connection over the Internet. In certain implementations, theuser device 11 may communicate with a wired or wireless networkconnection (e.g., a network connection over Wi-Fi, Bluetooth, Ethernet,cellular data connections). In still further implementations,functionalities of one or both of the interface 22 and the interfaceserver 23 may be implemented by the user device 11 (e.g., as a programor application executing on the user device). In other embodiments, theinterface is a voice interface, a mobile phone application, a chatbotintegration, or other user interfaces to web and/or cloud solutions.

Additional or alternative inputs may be received by the interface 22.For example, the interface 22 may additionally or alternatively receiveinformation regarding a pet breed, a pet weight, a pet activity level, apet gender, a pet gender status (e.g. spayed/neutered), a petenvironment/climate, current/previous supplements taken by the pet,current/previous pet diet, eating habits, desired health outcomes, auser and/or pet ingredient preference, and/or a shopping preference. Theadditional information regarding the pet may be provided by a user (e.g.pet owner or veterinarian). The additional information may also beobtained from a historical database or even received remotely fromdevices on the pet (e.g. collar) or in the pet environment (e.g. foodbowl or litter box).

In an embodiment where the interface 22 receives the pet image 3, thepet image 3 may be communicated to a pet image recognition model 33. Thepet image recognition model 33 may analyze the pet image 3 to determinethe pet species/breed, the life stage of the pet, and/or the size of thepet by analyzing the pet image 3 for one or more pet characteristics.The pet image recognition model 33 may produce a pet image recognitionresult 32. The pet image recognition result 32 may include the breed/petsize variance, the body condition variance, the activity variance, thepet weight variance and/or the life stage variance. The pet imagerecognition result 32 may then be communicated back to the interface 22.

In certain implementations, as depicted in FIG. 1 , the pet imagerecognition model 33 may be implemented on a cloud server 31. Forexample, the pet image recognition model 33 may be implemented by acloud server provided by a cloud services provider (e.g. Google CloudPlatform Services).

The system 5 may then communicate the pet image recognition result 32 toa pet food recommendation model 63. In certain implementations, thiscommunication may be indirect. For example, as depicted in FIG. 1 , thecloud server 31 and/or the image recognition model 33 may communicatethe pet image recognition result 32 to the pet food recommendation model63 indirectly via the interface 22 and/or the interface server 23. Insome implementations, the cloud server 31 and/or the image recognitionmodel 33 may communicate the pet image recognition result 32 to the petfood recommendation model 63 directly (e.g., via a communication linkbetween the cloud server 31 and the cloud server 61).

The pet food recommendation model 63 may then use the pet imagerecognition result 32 and/or other information provided from the user orstored by the pet food recommendation model 63 to determine a pet foodrecommendation result 62. The pet food recommendation result 62 may beselected by the pet food recommendation model 63 from a list of pet foodproducts, for example pet foods 62 a, 62 b, 62 c, and 62 d by analyzingthe image recognition result 32. For example, and as explained ingreater detail below, the pet food recommendation model 63 may calculateone or more of the breed/pet size variance, the body condition variance,the life stage variance, the activity level variance, and the pet weightvariance based on pet characteristics included within the imagerecognition result 32 or provided by the user 2.

In an embodiment, the pet food 62 a may comprise a high-fat pet food,the pet food 62 b may comprise a gluten-free pet food, the pet food 62 cmay comprise a low-sugar pet food, and the pet food 62 a may comprise ameat-free pet food. When the pet food recommendation result 62 isdetermined by the pet food recommendation model 63, the pet foodrecommendation result 62 may then be communicated to the interface 22.In other implementations, the pet food recommendation result 62 mayinclude more than one pet food 62 a-d. For example, the pet foodrecommendation model 63 may select more than one pet food 62 a, 62 b, 62c, and 62 d that meets the provided preferences of the user and mayinclude the selected pet foods 62 a, 62 b, 62 c, and 62 d in the petfood recommendation result 62 for selection by the user. In someembodiments, when multiple pet food products are presented as part ofthe pet food recommendation, the products can be prioritized based ondocumented logic within the model.

In certain implementations, as depicted in FIG. 1 , the pet foodrecommendation model 63 may be implemented on a cloud server 61. Forexample, the pet food recommendation model 63 may be implemented by acloud server 61 provided by a cloud services provider (e.g. Google CloudPlatform Services).

The interface 22 may use the pet food recommendation result 62 tosuggest the recommended pet food 21 to the user 2. The interface 22 mayfurther provide a link to purchase the recommended pet food 21, alongwith a suggested feeding schedule based on the pet image recognitionresult 32 and the pet food recommendation result 62.

The interface 22 may collect data regarding the pet image recognitionresult 32 and the pet food recommendation result 62. For example, theuser 2 may be able to provide feedback relating to the recommended petfood 21, such as the impact of the recommended pet food 21 on weightgain and/or loss of the pet 1, on changes in appearance of the pet 1and/or whether the pet 1 seemed to enjoy eating the recommended pet food21. In addition, the interface 22 may receive data from other sources,including smart collars used to track the movement of the pet 1, smartfeeding bowls used to feed the pet 1, smart scales to record a weight ofthe pet, veterinary history of the pet 1, subsequent pet imagerecognition results 32 for the pet 1 and/or updated preferences for thepet 1 input by the user 2. Such feedback may be generally referred to as“historical data” for training and optimization of the pet foodrecommendation model 63.

The historical data for training and optimization of the pet foodrecommendation model may be stored in a historical database 101. Thehistorical data for training and optimization of the pet foodrecommendation model may be used in the pet food recommendation model 63to provide the recommended pet food 21 most likely to satisfy therequests and preferences of the user 2 and/or the needs of the pet 1, asexplained in greater detail below.

In certain embodiments, the system 5 may include one or more componentsimplemented by a computer system including a CPU and a memory. Forexample, one or more components of the system 5 may include a memorystoring instruction which, when executed by a CPU, cause the CPU toimplement one or more functions of the component. As another example,one or more of the user device 11, interface server 23, cloud servers31, 61, and historical database 101 may be implemented as at least onecomputer system. In certain implementations, each of these componentsmay be implemented by a separate computer system. In otherimplementations, one or more of the components may be implemented by asingle computer system. For example, the interface server 23 and userdevice 11 may be implemented by the same computer system. As anotherexample, the cloud servers 31, 61 may be implemented by the samecomputer system in certain implementations.

Additionally, one or more components of the system 5 may communicateover a network, such as a public or private network. For example, theuser device 11, interface server 23, cloud servers 31, 61, andhistorical database 101 may communicate over the Internet. Thecomponents may communicate over one or more wired or wireless networkinterfaces, including, but not limited to, Ethernet, Wi-Fi, Bluetooth,and cellular data networks.

FIG. 1 also generally illustrates an embodiment of a method forproviding the recommended pet food 21 to the user 2. The method may beimplemented on a computer system, such as the system 5. For example, themethod may be implemented by the user device 11, interface server 23,cloud servers 31, 61, and historical database 101. The method may alsobe implemented by a set of instructions stored on a computer readablemedium that, when executed by a processor, cause the computer system toperform the method. For example, all or part of the method may beimplemented by a CPU and a memory.

Although the examples below are described with reference to theflowchart illustrated in FIG. 1 , many other methods of performing theacts associated with FIG. 1 may be used. For example, the order of someof the steps may be changed, certain steps may be combined with othersteps, one or more of the steps may be repeated, and some of the stepsdescribed may be optional. In certain implementations, the steps of themethod may preferably be performed in the depicted order withoutintervening steps.

In Step 10, the interface 22 may receive a notification from the user 2(e.g., a notification submitted via the user device 11) that the user 2wishes to be provided with the recommended pet food 21.

In Step 20, the interface 22 may receive preference information, such asthe user 2's or pet 1's grain preference, protein preference, foodtexture preference, natural ingredient preference, and/or shoppingpreference. The interface 22 may use this information to personalize thepet food recommendation result 62 for the pet and the pet owner, and/orother pet and/or pet food preferences. The interface 22 may also beconfigured to receive the pet image 3 of the pet 1. As further explainedbelow, the interface 22 may also receive data about the pet 1, such asdata regarding one or more of the pet 1's sex, breed, or age.

In Step 30, the interface 22 may communicate the pet image 3 to the petimage recognition model 33. The pet image recognition model 33 mayanalyze the pet image 3 to determine the pet image recognition result 32based on the pet image 3. For example, the pet image recognition model33 may analyze the pet image 3 to identify one or more imagecharacteristics of the pet image 3. For example, the pet imagerecognition model 33 may be implemented as a machine learning model,such as a two-dimensional machine learning model (e.g., a recurrentneural network or a convolutional neural network). The pet imagerecognition model 33 may be trained on training pet images, which may beprepared or otherwise collected from previously-submitted pet images 3,as discussed below. During training, the pet image recognition model 33may be configured to identify the image characteristics or may otherwisedevelop the image characteristics for identification within the petimage 3 based on the training process. Example image characteristics mayinclude the outline of the pet 1, the area enclosed by the outline ofthe pet 1, the percentage of pixels of the pet image 3 occupied by thepet 1, and one or more geometric dimensions of the pet 1 within the petimage 3 (e.g., length, height, distance from foot to shoulder, distancefrom back to stomach, chest breadth, chest depth).

The image characteristics may then be analyzed to generate the imagerecognition result 32. The image recognition result 32 may include oneor more pet characteristics of the pet 1, such as the height, weight,length, breed, breed size, pet size, activity level, life stage, and/orbody condition of the pet 1. The image characteristics may suggest orotherwise indicate the presence of one or more pet characteristics ofthe pet 1. In certain implementations, for example, during training thepet image recognition model 33 may identify a relationship between theoutline of the pet 1 within the pet image 3 and one or more petcharacteristics of the pet 1. For example, a larger boundary may suggestone or both of a higher weight for the pet 1 or a larger breed size forthe pet 1. As another implementation, the pet image recognition model 33may identify a relationship between the size or coloring of the pet 1 inthe pet image 3 and the breed of the pet 1. For example, the pet imagerecognition model 33 may determine that a pet 1 that is large andspotted black and white is a Dalmatian based on previous training. Inanother embodiment, the pet image recognition model may take otherfeatures of the image into consideration, such as, coat characteristicsand/or facial features to determine a breed of a pet.

The pet image recognition model 33 may be configured to generate orpredict certain pet characteristics depending on the pet characteristicsfor inclusion within the pet image recognition result 32 that arerelevant to the pet food recommendation model's 63 subsequentprocessing. For example, the pet food recommendation model 63 maycalculate a breed size variance and may therefore rely on an estimationof the breed size of the pet 1 in generating a pet food recommendationresult. In such an implementation, the pet image recognition model 33may therefore be configured to estimate the breed size of the pet 1based on the pet image 3 and to include the breed size estimation withinthe image recognition result 32.

In certain implementations, the pet image recognition model 33 may beimplemented as more than one machine learning model. For example, thepet image recognition model 33 may include a first machine learningmodel configured to identify the image characteristics of the pet image3 and a second machine learning model configured to analyze the imagecharacteristics and identify the pet characteristics of the pet 1. Instill further implementations, the pet image recognition model 33 may beimplemented by a single machine learning model. For example, the petimage recognition model 33 may include a single machine learning modelconfigured to both identify the image characteristics within the petimage 3 and to estimate the pet characteristics of the pet 1 based onthe image characteristics. In yet another example, the pet imagerecognition model 33 may not estimate the image characteristics and mayinstead include a single machine learning model configured to estimatethe pet characteristics of the pet 1 based on the pet image 3 directly.

In Step 40, the pet image recognition result 32 may be communicated tothe interface 22. In an embodiment, the image recognition result 32 maybe communicated to the pet food recommendation model 63 without firstbeing communicated to the interface 22.

In Step 50, the pet food recommendation model 63 may receive the petimage recognition result 32. The pet image recognition result 32 may bereceived from the interface 22, the pet image recognition model 33,and/or another location.

In Step 60, the pet food recommendation model 63 may analyze the petimage recognition result 32 to determine the pet food recommendationresult 62. The pet food recommendation model 63 may analyze petcharacteristics included in the pet image recognition result 32. The petfood recommendation model 63 may analyze pet characteristics included inthe pet image recognition result and data inputs provided to theinterface 22 to determine the pet food recommendation result. Forexample, the image recognition result 32 may include one or more of thebreed, breed size, pet size, body condition, life stage, activity level,and weight of the pet 1 and the data inputs may include one or more ofpet gender, pet gender status, weight of pet, sensitivity of pet, healthcondition of pet, environment of pet, current/previous supplement use,current/previous diet, eating habits, and/or desired health outcomeswhich the pet food recommendation model 63 may use to calculate one ormore variances, such as the breed/pet size variance, the body conditionvariance, the life stage variance, the activity level variance, and/orthe pet weight variance. The pet food recommendation model 63 may thenscore pet food products based on a sum of the one or more variances.

In certain implementations, the pet food recommendation model 63 may beconfigured to (i) filter a list of pet food products to create afiltered list of pet food products based on one or more user preferencesand (ii) sort the filtered list of pet food products. For example, auser 2 may provide one or more preferences to filter the pet foodproducts provided in the pet food recommendation result. In someembodiments, a user 2 may provide a grain preference, a proteinpreference (e.g. chicken, beef, pork), a food texture preference (e.g.wet vs dry), a natural ingredient preference, a shopping preference,and/or other additional variables that may be useful to create afiltered list of pet food products. The filtered list of pet foodproducts may then be sorted using one or multiple criteria chosen by theuser, for example, price, availability, and/or brand.

In still further implementations, or in addition to the aboveimplementations, the pet food recommendation model 63 may include amachine learning model that generates the pet food recommendation result62. For example, the machine learning model may be trained to determinethe prioritization factors of the one or more variances.

The pet food recommendation result 62 may be the recommended pet food 21selected from the pet foods 62 a-d. In other implementations, asdiscussed above, the pet food recommendation result 62 may include aplurality of pet foods 62 a-d for selection by a user 2.

In certain embodiments, the pet food recommendation result 62 may alsoinclude a pet feeding recommendation. For example, after the pet foods62 a-d have been identified, the pet food recommendation model 63 maydetermine a pet feeding recommendation based on the caloric density ofthe pet foods 62 a-d. For example, the pet food recommendation model 63may determine a feeding recommendation based on a combination offactors, including desired caloric intake and/or caloric density of theproduct selected for the pet 1 based on pet characteristics of the pet 1(e.g., the pet breed, the breed size, body condition, life stage,activity level, and/or weight of the pet 1). The pet food recommendationmodel 63 may then determine how much of each pet food 62 a-d is requiredto reach the desired caloric intake of the pet 1 based on the caloricdensity (e.g., by dividing the desired caloric intake by the pet foods'62 a-d caloric density). The pet food recommendation model 63 may thensplit the food into a certain number of desired or recommended meals perday (e.g., 2 meals per day) for the pet 1. In certain implementations,the pet food recommendation model may not generate the pet feedingrecommendation until after a user has selected a pet food 62 a-d fromthe pet foods 62 a-d included within the pet image recognition result.For example, the pet food and feeding recommendation can be directed toachieving an ideal body condition score within a defined period of time(e.g. 6 weeks or 10 weeks) as determined by weight maintenance, weightloss or weight gain.

In Step 70, the pet food recommendation result 62 may be received by theinterface 22. In one embodiment, the interface is a web interface. Inother embodiments, the interface is a chat, voice, or mobileapplication.

In Step 80, the pet food recommendation result 62 may be communicatedfrom the interface 22 to the user device 11 and/or to the user 2. Thepet food recommendation result 62 may be in the form of the recommendedpet food 21. In an embodiment, the recommended pet food 21 is two ormore different pet foods. In an embodiment, the recommended pet food 21is two or more pet food brands. In an embodiment, the recommended petfood 21 is a single pet food and/or a single pet food brand.

In Step 90, a historical database 101 may receive behavioral data fortraining and optimization of the pet food recommendation model 63 and/orthe image recognition model 33. For example, the pet image 3 may bestored in a historical database and may be manually tagged or analyzedand provided to the image recognition model 33 for training. In certainembodiments, a subject matter expert may review the pet image 3 toidentify one or more pet characteristics of the pet 1 or imagecharacteristics of the pet image 3 and may tag the pet image 3 with theidentified pet characteristics. The image recognition model 33 may thenbe trained on the pet image 3, in combination with a plurality of otherpet images 3. For example, the image recognition model 33 may adjustitself (e.g., one or more weights or factors within the imagerecognition model) to maximize the accuracy of the detected imagecharacteristics or pet characteristics.

In another example, the food recommendation model 63 may analyzebehavioral data of the user 2 or the pet 1 (e.g., whether the user 2purchased the recommended pet food 21, subsequent weight loss or gain bythe pet 1, subsequent pet 1 activity level) and may update one or moremodel parameters (e.g., the prioritization factors for the variances) tomaximize the accuracy of the recommended pet food 21. Such data may comefrom user interaction with the solution via web activity tagging,cookies, e.g., user surveys, purchase records, andsubsequently-submitted pet images 3. The food recommendation model 63may also analyze behavioral data about the pet 1 (e.g., whether the pet1 achieved desirable changes in body condition, whether the pet 1 likethe recommended pet food 21). Such data may come from connected petdevices (e.g., collars, bowls, scoops, litter boxes, water dispensers,cameras, scales, bins, mats, beds) that automatically collect andtransmit data used to provide the personalized pet food recommendationsand/or pet feeding recommendations. In certain implementations, the foodrecommendation model 63 may be optimized to improve one or more ofwhether the user 2 purchases the recommended pet food 21, whether thepet 1 experiences a desired change in weight after eating therecommended pet food 21, whether the pet 1 likes the recommended petfood 21, and whether the user continues to utilize the system 5 togenerate pet food recommendation results 62.

In Step 100, the behavioral data for training and optimization of thepet food recommendation model 63 may be communicated to the pet foodrecommendation model 63 to provide the recommended pet food 21 mostlikely to satisfy the requests and preferences of the user 2 and/or theneeds or preferences of the pet 1.

FIG. 2 generally illustrates an embodiment of an application diagram200. The application diagram 200 may represent what the user 2 interactswith on the user device 11, shown in FIG. 1 , to provide inputs to theinterface 22, shown in FIG. 1 .

Referring back to FIG. 2 , an introduction 210 may be configured toreceive background or baseline information from the user 2. For example,the introduction may receive login information, initial accountpreferences, required legal and/or data disclosure documents, and/orother general information from the user 1. The introduction 210 may onlybe used during an initial login to the interface 22, and may not bepresented during subsequent logins after an initial user profile iscreated.

The interface 22 may provide a menu 220. The menu 220 may be the “home,”“base,” or initial interface presented to the user 2 when logged intothe interface 22. The menu 220 may provide navigation elements to enablethe user 2 to navigate to other locations within the interface 22.

The interface 22 may provide one or more menu options 230. The menuoptions 230 may be accessible from the menu 220. The menu options 230may enable the user 2 to edit, set, and/or adjust settings in theinterface 22 and/or access other information relating to the interface22. For example, the menu options 230 may include screens and/ordocuments relating to frequently asked questions, contacts and/orcontact information, feedback pages (e.g., user editable feedback, oralready provided feedback by other users for the user 2 to review), aprivacy policy, terms and conditions, and/or an explanation relating toads on the interface 22.

The interface 22 may provide one or more profile inputs 240. The profileinput 240 may be accessible from the menu 220. The profile input 240 mayenable the user 2 to enter information relating to the pet and/or thepreferences of the user 2 that may be used to determine the recommendedpet food 21. For example, the profile input 240 may include: a startscreen, which may give instructions for how the user 2 is to proceed; abasic information screen; a photo upload screen, where the user 2 mayupload a pet image 3 for later use in the image recognition model 33,shown in FIG. 1 ; a breed information screen, where the user 2 can inputinformation about the breed of the pet 1; a weight information screen,where the user 2 can input information about the weight of the pet 1; anactivity level screen, where the user 2 can input information about theactivity level of the pet 1; an ingredient preference screen, where theuser 2 can input information about the ingredient preferences of the pet1 and/or the user 2; a protein preference screen, where the user 2 caninput information about the protein preferences of the pet 1 and/or theuser 2; and/or a shopping preference screen, where the user 2 can inputinformation about the shopping preferences of the user 2.

When at least one of the items in the profile input 240 is completed, afull profile 250 may be assembled using the information provided in theprofile input 240 and/or any information generated by the pet imagerecognition model 33, the pet food recommendation model 63, and/or thehistorical database 101, as shown in FIG. 1 . The full profile 250 maybe used to generate the recommended pet food 21. For example, one ormore characteristics included within the image recognition result 32 maybe included within the full profile 250. The pet food recommendationmodel 63 may then analyze one or both of the image recognition result 32and the full profile 250 to generate a pet food recommendation result62, as described above.

In an embodiment, if the photo upload is available in the profile input240, the profile input 240 may further include a file upload where theuser can upload the pet image 3 and/or receive messages regarding theparameters of the photo if the photo does not meet certain predefinedparameters. In certain implementations, the pet image 3 may require oneor more photo characteristics. For example, in certain implementations,the image recognition model 33 may be configured to provide the mostaccurate results based on pet images 3 taken with the pet 1 facingtoward the camera (e.g., the image recognition model 33 may be trainedprimarily on pet images 3 with the pet 1 facing the camera). In otherexamples, the image recognition model 33 may be configured to providethe most accurate results based on pet images 3 taken with the pet 1 inprofile (e.g., the image recognition model 33 may be trained primarilyon pet images 3 with the pet 1 in profile relative to the camera). Thephoto upload may specify the photo characteristics required or preferredfor the image recognition model 33 to produce an accurate imagerecognition result 32. Additionally or alternatively, an analyzing photovisual may be provided to the user when the pet image recognition model33 is processing the pet image 3 to determine the pet image recognitionresult 32.

The interface 22 may be provided with a thinking visual 260. Thethinking visual 260 may be provided to the user when the pet imagerecognition model 33 is processing the pet image 3 and/or any other datainputs to determine the pet image recognition result 32, the pet foodrecommendation result 62, and/or the recommended pet food 21.

The interface 22 may be provided with results 270. The results 270 maybe provided to the user 2 after the pet image recognition model 33 hasprocessed the pet image 3 and/or any other data inputs to determine thepet image recognition result 32 and/or the pet food recommendationresult 62. The results 270 may include the recommended pet food 21. Theresults 270 may include a link to access a feeding guide 280. Asdescribed above, in certain implementations the pet food recommendationresult 62 may include a plurality of pet foods 62 a-d from which theuser may select the recommended pet food 21 from among the pet foods 62a-d. In such implementations, the results may include a food selectionin which the user selects the recommended pet food.

The feeding guide 280 may include a feeding guide based on therecommended pet food 21, a pet food selected by the user, the pet image3 and/or any other data inputs to determine the pet image recognitionresult 32 and/or the pet food recommendation result 62.

The interface 22 may be provided with tips 290. The tips 290 may beintegrated into any of the other visuals discussed herein, and mayprovide the user 2 with recommendations or clarifications as the user 2navigates through options included in the interface 22.

In some embodiments, the user may create a profile to store informationabout their pet, preferences, and buying history. This profile can beused to drive algorithmically powered programs that trigger futurecommunication and interaction with the user. For example, thecommunication can be automated email or text message prompting the userto change the pet food or feeding recommendation when the pet reaches adefined life stage (e.g. adult). Machine learning capabilities can beapplied to data captured on a device, for example, a connected device onthe pet or user generated inputs in response to automated communicationto continually enhance the quality and personalized nature of the petfood and feeding recommendations for the individual pet and/or petowner.

FIG. 3 generally illustrates an embodiment of the customized feedingguide 330 for providing personalized pet food recommendations and/or petfeeding recommendations. As shown in FIG. 3 , the customized feedingguide 330 is adapted for use on a web based and/or mobile application,although the customized feeding guide 330 may be provided on othermedia. The customized feeding guide 330 may include: a pet foodselection 301; a pet profile 302; pet feeding instructions 303; pet foodtransition instructions 304; a product overview 305; product ingredientand nutrition information 306; and other pet food recommendations 307.The pet food selection may include pet's name, brand name, product nameand an option to navigate to a website to purchase the pet food. The petfood selection 301 may also indicate the unit size, and/or style of petfood selected by the user 2. The pet profile 302 may reflect data inputsand generated information relating to the pet 1. For example, suchinformation may include the pet breed, the pet weight, the pet activitylevel, the user and/or pet ingredient preference, pet proteinpreference, and/or the shopping preference. The pet feeding instructions303 may include directions for feeding the pet 2. The pet feedinginstructions 303 may consider the pet image 3 and/or any other datainputs to determine the pet image recognition result 32 and/or the petfood recommendation result 62, as well as the pet image recognitionresult 32 and/or the pet food recommendation result 62.

The pet food transition instructions 305 may include instructions forhow to safely and successfully transition the pet 1 from a first food toa second food. The other pet food recommendations 307 may include otherpet foods 62 a, 62 b, 62 c, and 62 d identified by the pet foodrecommendation model 63, but which the user 2 did not select.

It should be understood that various changes and modifications to theexamples described here will be apparent to those skilled in the art.Such changes and modifications can be made without departing from thespirit and scope of the present subject matter and without diminishingits intended advantages. It is therefore intended that such changes andmodifications be covered by the appended claims. Further, the presentembodiments are thus not to be limited to the precise details ofmethodology or construction set forth above as such variations andmodification are intended to be included within the scope of the presentdisclosure. Moreover, unless specifically stated any use of the termsfirst, second, etc. do not denote any order or importance, but ratherthe terms first, second, etc. are merely used to distinguish one elementfrom another.

What is claimed is:
 1. A method comprising: receiving, by a webinterface server, a pet image depicting a pet; analyzing, by the webinterface server using a pet image recognition model, the pet image todetermine one or more pet characteristics of the pet, including:training the pet image recognition model to identify an imagecharacteristic based on a percentage of pixels of the pet image occupiedby the pet; and identifying the one or more pet characteristics of thepet using the trained pet image recognition model; receiving, by the webinterface server, one or more pet-related data inputs; storingbehavioral data of the pet in a historical database; training, by theweb interface server, a pet food recommendation model based on thebehavioral data stored in the historical database; and analyzing, by theweb interface server using the pet food recommendation model, the petcharacteristics and pet-related data inputs to generate a pet foodrecommendation and/or pet feeding recommendation for the pet.
 2. Themethod of claim 1, wherein the pet image is received from a user device.3. The method of claim 1, wherein analyzing the pet image furthercomprises: analyzing, by the web interface server, the pet image toidentify one or more image characteristics of the pet image; anddetermining, by the web interface server, the one or more petcharacteristics based on the image characteristics.
 4. The method ofclaim 3, wherein the pet characteristics comprise one or morecharacteristics selected from the group consisting of a breed, a breedsize, a pet size, a body condition, a life stage, an activity level, apet gender, a pet gender status, and a weight of the pet.
 5. The methodof claim 1, further comprising: filtering, by the web interface server,a list of pet food products based on at least one user preference tocreate a filtered list of pet food products.
 6. The method of claim 5,wherein the at least one user preference comprises at least onepreference selected from the group consisting of a grain preference,protein preference, a food texture preference, a natural ingredientpreference, and a shopping preference.
 7. The method of claim 1, furthercomprising: calculating, by the web interface server, one or morevariances selected from the group consisting of a breed size variance, apet size variance, a body condition variance, a life stage variance, anactivity level variance, and a pet weight variance.
 8. The method ofclaim 1, further comprising: calculating, by the web interface server, aplurality of variances to generate a plurality of calculated variances;and scoring, by the web interface server, a list of pet food productsbased on a sum of the calculated variances to identify one or morerecommended pet food products.
 9. The method of claim 1, furthercomprising: presenting, by the web interface server, the pet foodrecommendation to a user, wherein the pet food recommendation contains aplurality of pet food products; receiving, by the web interface server,a selection from the user of a selected pet food product from theplurality of pet food products; and generating, by the web interfaceserver, a pet feeding recommendation based on the selected pet foodproduct.
 10. The method of claim 9, wherein the pet feedingrecommendation is generated based on a caloric density of the selectedpet food product.
 11. The method of claim 1, further comprising:training the pet image recognition model based on the behavioral datastored in the historical database.
 12. A system comprising: a processor;and a memory storing instructions which, when executed by the processor,cause the processor to: receive a pet image depicting a pet; analyze,using a pet image recognition model, the pet image to determine one ormore pet characteristics of the pet, including: training the pet imagerecognition model to identify an image characteristic based on apercentage of pixels of the pet image occupied by the pet; andidentifying the one or more pet characteristics of the pet using thetrained pet image recognition model; receive one or more pet-relateddata inputs; store behavioral data of the pet in a historical database;train a pet food recommendation model based on the behavioral datastored in the historical database; analyze, using the pet foodrecommendation model, the pet characteristics and pet related datainputs to generate a pet food recommendation and/or pet feedingrecommendation for the pet.
 13. The system of claim 12, wherein thememory stores further instructions which, when executed by theprocessor, cause the processor to: analyze the pet image to identify oneor more image characteristics of the pet image; and determine the one ormore pet characteristics based on the image characteristics.
 14. Thesystem of claim 12, wherein the memory stores further instructionswhich, when executed by the processor, cause the processor to: filter alist of pet food products based on at least one user preference tocreate a filtered list of pet food products.
 15. The system of claim 14,wherein the pet characteristics comprise one or more characteristicsselected from the group consisting of a breed, a breed size, a pet size,a body condition, a life stage, an activity level, a pet gender, a petgender status, and a weight of the pet.
 16. The system of claim 12,wherein the memory stores further instructions which, when executed bythe processor, cause the processor to: calculate one or more variancesselected from the group consisting of a breed size variance, a pet sizevariance, a body condition variance, a life stage variance, an activitylevel variance, and a pet weight variance.
 17. The system of claim 12,wherein the memory stores further instructions which, when executed bythe processor, cause the processor to: calculate a plurality ofvariances to generate a plurality of calculated variances; and score alist of pet food products based on a sum of the calculated variances toidentify one or more recommended pet food products.
 18. The system ofclaim 12, wherein the memory stores further instructions which, whenexecuted by the processor, cause the processor to: present the pet foodrecommendation to a user, wherein the pet food recommendation contains aplurality of pet food products; receive a selection from the user of aselected pet food product from the plurality of pet food products; andgenerate a pet feeding recommendation based on the selected pet foodproduct.
 19. The system of claim 12, wherein the memory stores furtherinstructions which, when executed by the processor, cause the processorto: train the image recognition model based on the behavioral datastored in the historical database.
 20. A non-transitory,computer-readable medium storing instructions which, when executed by aprocessor, cause the processor to: receive a pet image depicting a pet;analyze, using a pet image recognition model, the pet image to determineone or more pet characteristics of the pet, including: training the petimage recognition model to identify an image characteristic based on apercentage of pixels of the pet image occupied by the pet; andidentifying the one or more pet characteristics of the pet using thetrained pet image recognition model; receive one or more pet-relateddata inputs; store behavioral data of the pet in a historical database;train a pet food recommendation model based on the behavioral datastored in the historical database; and analyze, using the pet foodrecommendation model, the pet characteristics and pet-related datainputs to generate a pet food recommendation for the pet.